Research On Consumers’ Online Shopping Decision-making For Knowledge-based Recommendation Service

With the development of Internet and information technologies, e-commerce in our country is increasingly developing, and consumers’ online shopping is booming, which offers e-commerce businesses unprecedented opportunities. While the rapid development of e-commerce provides consumers a huge selection of commodity options and information, it also brings about enormous cognitive load. That’s why many websites put forward all kinds of recommendation services, such as collaborative recommendation, recommended hot commodity, commodity classification of portfolio selection, and so on, in order to reduce the blindness of the consumer decision-making in the ocean of goods.But so far, it is a widespread issue that consumers are quite in lack of commodity-related knowledge, especially for the higher price of high-tech product selection process. Because of the direct interests of purchase, consumers may show a more rational decision-making intention, so they should hope that the website can provide commodity purchasing knowledge of relevant recommendation service, so as to improve the decision-making efficiency.Under this background, this paper puts forward the idea of online recommendation service of knowledge, namely on the bases of the original commodity recommendation, whether we can also provide consumers with aided knowledge about commodity in order to make a decision? For example, we can provide with commodity parameter profile which on the utility perspective, what more, we can make recommendations according to the preference of consumer. Further still we can provide commodity parameter knowledge which has been analyzed and processed. For example, the Cost-effective of commodity knowledge, so as to reduce the cognitive load in consumer decision making.Therefore, this paper provide academic basis for this kind of knowledge recommendation service conception, namely in this paper we will analysis the online consumer shopping decision-making mechanism as well as knowledge in the role of analyzing the mechanism, according to the relevant decision-making theory, cognitive theory and experimental analysis, that provide the basis for accurate design of the knowledge of recommended services, we also need to analyze how consumers can be more effective to accept future recommendation service of knowledge, so to provide a theoretical basis for the overall framework design of recommendation service of knowledge.Based on a comprehensive review of relevant research at home and abroad (see the second chapter), this paper focuses on the following scientific issues: (1)What is the mechanism of online decision-making? Among them, the analysis of the internal mechanism of commodity knowledge caused consumer merchandise selection decision is the key, specific knowledge to support select what part of the decision-making in the consumer goods? What is the supporting mechanism? On this basis, the possible intention and reason of accepting and studying recommendation service of knowledge were analyzed, including whether every consumer will need kind of this kind of recommendation service of knowledge. And whether the consumer knowledge background, gender, cognitive demand are related? The resultant solution is needed to construct the recommendation service of knowledge.This part is also the creative point of this paper. the current literature, the commodity purchase decision support in knowledge study, is mainly concentrated in the influence of brand knowledge on consumers’ purchase decision behavior, including how the brand awareness, brand image to influence consumers’ trust and satisfaction on brand, then influence the relationship of the consumer and the brand. However mechanism of support in the selection of products in the decision-making aspects of knowledge has yet to be seen.In view of the above problems, this paper carried out the following work:①Based on the consumer decision-making behavior theory, decision-making theory and other related theories, we analyze the decision-making process of consumer online selection, and build the decision model for the decision-making process of consumer online selection;②Based on the information processing theory, demand-motivation-behavior theory and other related theories, we analyze the mechanism of knowledge support, the acceptance and learning of the commodity-related knowledge will and the reasons in the decision-making process of consumer online selection;③Based on questionnaire investigation, we conduct an empirical analysis the knowledge demand, acceptance and learning product-related knowledge in the decision-making process of consumer online selection.Through theoretical and empirical analysis, the main conclusions of this paper are:①Consumers generally require knowledge of the commodity for auxiliary on value choices, especially for high-tech commodities, so the high-tech commodities are the focus of recommendation service of knowledge;②Consumers are generally willing to accept and learn from external knowledge of related product, including online recommendation service, in order to assist in the choice of commodities, so recommendation service of knowledge will be welcome;③Most of consumers are willing to learn commodity parameters, in order to make a better choice, so that the vast majority of consumers do not exclude recommendation service of knowledge for commodities which load high cognitive, so the popular explanation of commodity parameter can become the basic content of recommendation service of knowledge k;④The consumers of different cognitive needs and sex have the differences in the choose of different levels of recommendation service of knowledge, therefore it is very necessary to provide different levels of recommendation service of knowledge to different characteristics of consumers.See the third chapter of the thesis.(2)How to observe the knowledge demand and decision behavior preference of consumers online selection decision through controlled experimental? Including designing the observation index of knowledge demand and decision making behavior preference in the commodity selection decision; how to observe the effects of individual factors on consumers’ knowledge demand and decision behavior preference through control experimental observation? The key observations are the effects of individual knowledge background, gender and cognitive demand. This paper will be the first attempt to use psychological measurement method on experimental subjects cognitive demand levels, and thus provide an important basis for the precise design of the knowledge of recommended services.In view of the above problems, this paper carried out the following work:①We design and develop experiment simulation platform for digital camera selection decision, in order to avoid the field experiment to bring all sorts of interference, which is also the innovation of this paper, previous research has not yet simulated actual electronic commerce website shopping environment in order to refine the observation and analysis of behavioral elements, specific including experimental interface design, parameter selection, parameter interpretation, cost-effective analysis of scoring, user comments, backstage record consumer decision-making behavior, in order to satisfy the demand of observation and analysis of consumer choice behavior;②We test cognitive demands levels (i.e., like thinking) on the basis of mental measurement experiment, and analysis the difference, a first step in the deepening of research in the future;③We use preference model and expert knowledge to design the index of degree expert knowledge, in order to observe the demand of subjects decision knowledge, namely the index is more small, the consumer is more far from the expert knowledge, the greater of the need of knowledge; ④we design index of type preference values of weight by using parameters of support degree and order of parameters, in order to observe the commodity parameter type preferences, namely preference value is greater, more consumers prefer this type of parameters;⑤Using the association rules mining method to design parameters of the cognitive path preference analysis program, in order to observe the commodity parameter cognitive rules. That is after choosing a parameter, whether there is some relationship with visiting the next parameter.The index of the design in the other model based on the combination of the research and gives some innovative features.Through empirical analysis, the main conclusions of this paper are:①for digital cameras goods, The overall consumer are lack of knowledge of the goods;②Different gender, background consumers have significant difference in the parameter type preferences, such as female consumers prefer the charm of parameters,while consumers of different cognitive levels of demand have no difference in the parameter type;③Consumers of different characteristics have some differences in the preferences of the parameters of cognitive path, among these consumers the parameters of cognitive path of consumers with high cognitive demand focused on the performance parameters and functional parameters;The above conclusions provide inspiration for the knowledge of recommended services design, See in particular4,5,7Chapter of the paper.(3) Finally, we analyze factors which influent the acceptance of recommendation service in the decision-making process, and get a comprehensive understanding the factors which influence the construction of the knowledge recommendation service, and provide advice for the design of the overall framework of the construction, so as to better assist consumers in the decision-making process of online shopping.This paper carried out the following work:①Based on the technology acceptance theory, take recommendation service of Amazon China as an example, we build model of factors which influence the efficient acceptance of the recommendation service;②we undertook a questionnaire survey of the model, in order to collect the using perceptual data about recommendation service of Amazon China;③Based on the experimental data, we conduct an empirical analysis of the model using structural equation model approach, and verified the hypothesis put forward. The analysis conclusions of usefulness and ease of use provide a basis for the knowledge of recommended service framework design, see in particular6,7Chapter of the paper.